Abstract #300989

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JSM 2003 Abstract #300989
Activity Number: 442
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Business & Economics Statistics Section
Abstract - #300989
Title: The Effect of Income Imputation on Poverty Measurement: The Approach of Nonparametric Bounds
Author(s): Andrea Regoli*+ and Claudio Quintano and Rosalia Castellano
Companies: University of Naples and Universitario Degli Studi Di Napoli and University of Naples
Address: Istituto Di Statistica E Matematica, Napoli, 80133, Italy
Keywords: missing data ; poverty line ; Manski's bounds approach
Abstract:

This paper deals with the study of the poverty incidence in the presence of incomplete data, following the bounds approach by Manski. Unlike the imputation procedures that are commonly used in order to compensate for missing data, this approach does not require any assumption on the randomness of nonresponses; it allows to derive lower and upper bounds for the measure of interest and, with the introduction of suitable restrictions, it enables to improve the precision of the estimates. The informative content of this method, which is inversely related to the width of the estimated interval, is then compared with the findings from an analysis of the complete dataset after a multiple imputation procedure is performed. The poverty analysis is carried out on the household income data from the Survey of Household Income and Wealth by the Bank of Italy, for the year 2000. The missing responses as well as the partial responses are artificially generated through a simulation technique that is intended to reproduce a reasonable response behavior to the income variable, by establishing a classification of the units in full respondents, partial respondents and nonrespondents.

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